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Record W4399265606 · doi:10.5539/ass.v20n3p87

Cultural Empowerment Models and Mechanisms in Rural Development: A Case Study in Zhang Village, China

2024· article· en· W4399265606 on OpenAlexvenueno aff
Siwei Yu, Tan Chee Seng, Ding Fan, Nor Zarifah Maliki, Ge Ma

Bibliographic record

VenueAsian Social Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsZhàngChinaEmpowermentEconomic growthRural developmentSocioeconomicsSociologyGeographyEconomics

Abstract

fetched live from OpenAlex

This study in Zhang Village, Muchuan County, Leshan City, China, examines cultural empowerment's role in stimulating rural endogenous development and outlines related development models and pathways. This research uses a multidimensional approach to analyze vital cultural elements in Zhang Village. The study uses Lasso regression analysis to quantitatively assess these cultural components' impact on rural development, supported by a five-year dataset from 2018 to 2022. The analysis incorporates a comprehensive dataset of cultural activities, facilities, industry outputs, and policy implementations in the village. Findings reveal that protecting traditional crafts, harnessing cultural industries' economic potential, and robustly implementing cultural policies are crucial to rural development. Cultural empowerment significantly enhances economic performance and community engagement. This enhancement promotes economic diversification and strengthens social cohesion and cultural identity. The study emphasizes the essential need to develop effective cultural policies that foster an environment conducive to sustainable rural development. It argues that integrating cultural aspects into comprehensive development strategies is critical. The research offers new theoretical insights and practical approaches, contributing to discussions on rural revitalization.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.274
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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